As the head of the Glassbeam sales team, there are few questions that I often hear from our customers that I would like to address. Below, you will find answers to the most common questions that we get about Glassbeam’s machine data analytics solution. In the first of this two-part series, I’ll address common questions about Glassbeam offerings, implementation and security. In part two, we’ll discuss Glassbeam and OEM service agreements. And of course, if you get to the end and find that you want to know more, we’re here to help.
We are now three quarters of the way through 2018 and very proud of our team’s many accomplishments this year. From new offerings and partnerships to certifications and industry event participation, we have made great strides towards our goal of expanding our advanced analytics offerings to include support for more healthcare equipment types and manufacturers.
In part one of this blog series, I introduced the Glassbeam EVM (Environmental Variable Monitoring) solution and explained the importance of using the EVM solution to collect environmental telemetry data such as compressor power, water inlet and outlet temperature, room humidity, and temperature readings every few seconds.
On the heels of a great event and presentation along with Rick Gaylord, our healthcare solution specialist, at the 2018 CEAI Conference, I want to continue the conversation about the far-reaching impacts of machine data and artificial intelligence for healthcare technology.
It is well known that margins in the medical device industry are eroding, partly due to the fact that nearly all health care providers are involved in some type of group purchasing organization (GPO) and that GPOs account for nearly three-quarters of provider purchases.
Most would consider analytics a science. The Glassbeam team considers analytics an art of combining impermeable truth from machine logs with deep healthcare domain expertise. As we expand our penetration of the healthcare market after spending years in the data center world, where the gold standard for machine uptime was 99.999 percent, we have recognized a huge opportunity since the acceptable machine uptime for medical equipment ranged from 90 to 97 percent.
Maximizing uptime of diagnostic equipment is vital to both patients and healthcare organizations. As medical imaging equipment becomes more sophisticated and the need for healthcare organizations to improve their availability becomes more acute, so does the value of machine log data and advanced analytics. Here, we’ve listed several important ways that hospitals can use machine log data and predictive/prescriptive analytics to optimize operational efficiency and revenues.
Medical imaging or diagnostic equipment such as Computed Tomography (CT), Ultrasound, and Magnetic Resonance Imaging (MRI) devices play a critical role in modern healthcare. But while these devices enable healthcare providers to better diagnosis their patients' and provide an optimal treatment plan, they are also very expensive to maintain.
In this section we will investigate how Glassbeam’s DSL called SPL (Semiotic Parsing Language) helps in parsing multi-structured machine logs.
SPL allows a log file to be treated as a hierarchical document consisting of multiple segments (or sections). Each hierarchical segment is called namespace. This allows for zeroing in on the exact section to parse specific elements from, thus localizing the scope of extracts.